In general, examples of a method for ensuring security and crime prevention include an artificial surveillance method performed by a person in charge of security and crime prevention, a remote surveillance method using a surveillance or monitoring camera, and a dangerous situation transmission method using a sensor of a terminal carried by an individual. Meanwhile, the remote surveillance method using a surveillance camera is divided into two schemes: a manual scheme in which a surveillant is a human and a fully-automatic scheme in which a surveillant is a mechanical system.
In the case of the artificial surveillance method, the artificial surveillance is performed by a relevant person who is in charge in crime prevention or personnel of a private security and anti-crime company in an anti-crime site.
However, at present, most countries encounter realistic problems in that they absolutely lack in relevant anti-crime personnel compared to personnel engaged in an area requiring security and crime prevention, and much economic burdens are imposed on the countries in order to entrust the supply of the relevant anti-crime personnel to private security and anti-crime companies.
In an attempt to solve such problems, most countries adopt a remote surveillance system using a surveillance camera installed on an anti-crime site. In addition, a service increases annually in which a security program is installed on a smartphone that is simple and convenient for an individual to carry so that personal security-related information is transmitted to a control PC of a security-related agency
However, the dangerous situation transmission method using a sensor of a terminal carried by an individual has a limitation in detection of a situation based on a sensor. Therefore, since it is difficult for the dangerous situation transmission method to expect to have a great effect, there is a need for a more accurate detection method in the personal security field.
An image surveillance system of detecting the occurrence of any particular act or accident is recognized as being most important in the research fields using human tracking.
The reason for this is that as a society advances, the importance of the safety of individuals and facilities are highly recognized in a public place as well as a personal space
As a modern society increasingly follows a trend toward informatization, unmannization, automatization, and computerization, the safety of individuals and the safety in a place of business continuously appear as important issues. Thus, efforts for protecting and managing the properties and the safety of individuals and the place of business have been made continuously. The importance and coverage of the security has been widened to major facilities, public offices, schools, enterprises, and private homes. Therefore, there is a need for the recognition of the importance and the development of the image surveillance system.
A typical example of such an image surveillance system includes a control (or surveillance) system using a network camera (CCTV). The CCTV control system is evolving drastically along with the development of an image input technology, an image transmission technology, and an image output technology.
An up-to-date CCTV control system outputs images inputted from a plurality of cameras on a plurality of monitors arranged inside a control center or arranges and outputs a plurality of lattice-shaped images on a single monitor. Then, an operator or a manager performs a surveillance activity while observing images outputted on the monitor.
By the way, the number of the network cameras used in the control system is increasing over time, thus leading to a great increase in the amount of the image data inputted to the control system. Such an increase in the image data acts as a transmission load in a network, thus resulting in a degradation of image quality.
For example, if the number of the network cameras is greater than 100, all of the images are not outputted on the monitor screen at one time, but are outputted in such a manner as to circulate the whole images. In addition, there occurs a problem in that a resolution of the images being outputted is degraded.
Further, it is difficult to know where two-dimensional images displayed on a monitor are located due to an increase in the number of the network cameras, which causes a problem in that the degree of understanding of images is decreased. Therefore, there is a need for the development of an intelligent control system for rapidly processing and efficiently monitoring video image data increasing by a user.
In the meantime, since a conventional surveillance method in which an operator or a manager performs a surveillance activity while observing images output on the monitor depends on a continuous observation by a human, it is inefficient and has a high risk of missing an abnormal situation as a surveillance target due to a reduction in the ability to concentrate. Therefore, the development and research of an intelligent surveillance system has been made continuously.
The intelligent surveillance system refers to a system that analyzes images inputted from a camera in real-time, and detects, tracks and sorts a moving object.
In particular, information on an object is provided to a manager in real-time by determining whether or not the object generates an event corresponding to a security polity, and a function of post management and prevention can be maximized after storing related data and event information.
A research on an up-to-date object detection and tracking system is made focusing on a particular scene or situation rather than the movement of an object. For example, an active shape model (ASM) has been proposed which analyzes the outer components of the object existing in an image based on a training set consisting of human-shaped models to estimate the most similar model in the training set.
In addition, in order to solve a problem such as an overlapping phenomenon, a model-based algorithm using extraction and analysis of a human-like silhouette from an image has been proposed, and a real-time blob (Blob) tracking algorithm using a human as a model has also been proposed.
As another conventional method, a method has been proposed which employs the support vector machine (SVM) to create a pattern and statistically analyze the pattern using wavelets as features of objects existing in an image. Also, a method has been proposed which separately creates a pedestrian pattern and a non-pedestrian pattern, respectively, using a simple and rapid Adaboost algorithm in order to recognize a pedestrian.
Besides, various methods have been proposed which effectively and accurately detects an object to attempt an access according to data analysis, but a research has not been sufficiently made yet on a method of analyzing an image while being targeted to a specific situation.
In this case, situation information regarding the specific situation generated by the object substantially includes all the information available at the time point when an interaction is made between users, and information that can be detected by an application as part of an application operation environment. In order to implement such an intelligent (smart) environment, situation recognition and information collection are needed by sensors having various kinds and functions such as temperature, humidity, illumination, pressure, acceleration, gradient, camera, infrared ray(IR), visible light, motion, magnetic fields, etc. Particularly, since the location of an object is important information for the purpose of situation recognition and information collection, a research on various services is in progress.
The recognition of who (i.e., an object) does what where is required to recognize a situation.
In order to recognize information on “who”, as of now, it is required to identify each ID of objects to be tracked as a factor critical in the pervasive computing environment.
In recent years, an automatic object recognition and tracking technology in the video sequence is applied in a variety of fields such as an unmanned surveillance system, an intelligent transportation system, a military system, etc. However, the automatic object recognition and tracking technology enables the grasping of the number of tracking object, and the recognition of a boundary line intrusion and a defined behavior pattern, but still entails a problem in that it cannot identify an ID of an object which it is desired to track.
Meanwhile, in order to recognize information on “what”, it is required to recognize a correct location and behavioral pattern. However, the information processing technology based on images has a difficulty in extracting features due to interference of light, shadow, and noise.
Therefore, there is an urgent need for the development of a situation information recognition technology that can more effectively recognize a situation of “who does what where” in order to construct a situation recognition system for implementing a variety of useful services.